Remote AI Data Quality & DB Administrator

prolificacademicltd; United States; Remote; USD 21,000 - 34,000; Full time

Benefits offered by this job

Remote work

Flexible tasks

$25 per hour potential

Job summary

prolificacademicltd in the United States is seeking professionals to contribute database and records expertise to training and evaluating AI systems through paid, task-based work. After a short skills assessment, successful applicants may be invited to review data outputs, label examples, and assess how well automated systems handle real-world data quality challenges.

Responsibilities include checking AI-generated entries for accuracy, creating test examples with messy data, and identifying

Qualifications

• Professional experience in high-volume data entry or records management.
• Strong accuracy and attention to detail with large datasets.
• Efficient typing and data processing skills.
• Understanding of data formats and validation rules.

Responsibilities

• Check AI-generated entries and extracted records for accuracy, completeness, and consistent formatting.
• Create examples with messy, ambiguous, or conflicting information to test data-entry handling.
• Identify categorization, labeling, and field-mapping errors in generated datasets.
• Classify and annotate samples to reinforce appropriate data structures and validation rules.
• Compare outputs with established quality standards and document issues clearly.

Skills

High-volume data entry

Data processing

Attention to detail

Typing speed

Quality assurance

Tools

Excel

Google Sheets

Airtable

SQL

Access

CRM

ERP

Confluence

Notion

Job description

prolificacademicltd in the United States is seeking professionals to contribute database and records expertise to training and evaluating AI systems through paid, task-based work. After a short skills assessment, successful applicants may be invited to review data outputs, label examples, and assess how well automated systems handle real-world data quality challenges.

Responsibilities include checking AI-generated entries for accuracy, creating test examples with messy data, and identifying